{
 "cells": [
  {
   "cell_type": "code",
   "id": "initial_id",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "import torch\n",
    "\n",
    "rotateMatrix= (torch.load('1-10-1 (1).pth', weights_only=True)).to('cuda').to(torch.float16)\n",
    "\n",
    "I = rotateMatrix @ rotateMatrix.T\n",
    "I = I.to(torch.float32)\n",
    "print(I[0:3,0:3])\n",
    "\n",
    "det = torch.det(I)\n",
    "det.item()"
   ],
   "outputs": [],
   "execution_count": null
  },
  {
   "metadata": {},
   "cell_type": "code",
   "source": [
    "def random_orthogonal_matrix(dim, device):\n",
    "    \"\"\"生成一个随机的正交矩阵，且行列式值为 1，并使用 float32 来执行 QR 分解\"\"\"\n",
    "    matrix = torch.randn(dim, dim, device=device)  # 使用 float32 来执行 QR 分解\n",
    "    Q, _ = torch.linalg.qr(matrix)  # 使用 QR 分解生成正交矩阵\n",
    "\n",
    "    # 修正行列式为 1\n",
    "    if torch.det(Q) < 0:\n",
    "        Q[:, 0] = -Q[:, 0]\n",
    "    return Q"
   ],
   "id": "f22e7fb283617541",
   "outputs": [],
   "execution_count": null
  },
  {
   "metadata": {},
   "cell_type": "code",
   "source": [
    "det = torch.det(random_orthogonal_matrix(11008, 'cuda'))\n",
    "det.item()\n"
   ],
   "id": "5a77b73782989486",
   "outputs": [],
   "execution_count": null
  },
  {
   "metadata": {},
   "cell_type": "code",
   "source": [
    "import torch\n",
    "from eval import show_without_plt\n",
    "from quantize_util import QuantizationUtils\n",
    "ffn = torch.load('ffn/second_line_input/0tensor0.pth', weights_only=True).to(torch.float32)\n",
    "quanter = QuantizationUtils(scale=2 / 255, zero_point=0)\n",
    "# print(ffn)\n",
    "quantile_min=0.0001\n",
    "quantile_max=0.9999\n",
    "\n",
    "print(torch.max(ffn).item(), torch.min(ffn).item())\n",
    "\n",
    "clipped_min = torch.quantile(ffn, quantile_min)\n",
    "clipped_max = torch.quantile(ffn, quantile_max)\n",
    "\n",
    "print(clipped_min.item(), clipped_max.item())\n",
    "\n",
    "ans = show_without_plt(ffn.to(torch.float32))\n",
    "print(ans[\"lower_ratio_1e_3\"], ans[\"lower_ratio_1e_4\"])\n",
    "\n",
    "k = torch.sum(ffn == 0).item() / torch.numel(ffn)\n",
    "print(k)\n",
    "\n",
    "ffn = quanter\n",
    "    \n",
    "    \n",
    "    \n",
    "    \n"
   ],
   "id": "dd5bd0225203a6c",
   "outputs": [],
   "execution_count": null
  },
  {
   "metadata": {},
   "cell_type": "code",
   "source": [
    "tensor = torch.tensor([1, 2, 3, 4, 5, 6, 7, 8, 9, 10])\n",
    "\n",
    "k=(tensor > 5)\n",
    "\n",
    "torch.sum(k)"
   ],
   "id": "fea85517677f2b65",
   "outputs": [],
   "execution_count": null
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-01-18T16:18:10.438443Z",
     "start_time": "2025-01-18T16:18:10.077981Z"
    }
   },
   "cell_type": "code",
   "source": [
    "\n",
    "import torch\n",
    "from eval import show_without_plt\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "# 中文显示\n",
    "import matplotlib\n",
    "matplotlib.rcParams['font.sans-serif'] = ['SimHei']\n",
    "matplotlib.rcParams['axes.unicode_minus'] = False\n",
    "\n",
    "load_tensors_1 = torch.load('original_input.pth', weights_only=True)\n",
    "load_tensors_2 = torch.load('modified_input.pth', weights_only=True)\n",
    "\n",
    "counts_1, counts_2 = [], []\n",
    "bins = []\n",
    "# 遍历加载的张量\n",
    "for tensor1, tensor2 in zip(load_tensors_1, load_tensors_2):\n",
    "    temp1 = show_without_plt(tensor1)\n",
    "    temp2 = show_without_plt(tensor2)\n",
    "    counts_1.append(temp1[\"counts\"])\n",
    "    counts_2.append(temp2[\"counts\"])\n",
    "    bins = temp1[\"bins\"]  # 假设两组数据的 bins 是一致的\n",
    "\n",
    "print(counts_1)\n",
    "print(counts_2)\n",
    "\n",
    "# 计算平均值\n",
    "count1_avg = np.array(counts_1).mean(axis=0)\n",
    "count2_avg = np.array(counts_2).mean(axis=0)\n",
    "\n",
    "print(count1_avg)\n",
    "print(count2_avg)\n",
    "\n",
    "# 转换区间为字符串形式\n",
    "bin_labels = [f\"{bins[i]}-{bins[i + 1]}\" for i in range(len(bins) - 1)]\n",
    "# print(bin_labels)\n",
    "\n",
    "# 绘制柱状图\n",
    "plt.figure(figsize=(16, 8))\n",
    "width = 0.4  # 每组柱子的宽度\n",
    "x = np.arange(len(bin_labels))  # x 轴的位置（以区间编号为中心）\n",
    "\n",
    "# 绘制两组柱子\n",
    "bars1 = plt.bar(x - width / 2, count1_avg, width=width, label=\"旋转前\", color='skyblue', edgecolor='black')\n",
    "bars2 = plt.bar(x + width / 2, count2_avg, width=width, label=\"旋转后\", color='salmon', edgecolor='black')\n",
    "title = \"featuremap全局平均数值分布\"\n",
    "plt.title(title, fontsize=15)\n",
    "\n",
    "# 设置横轴和纵轴标签\n",
    "plt.xlabel(\"区间\", fontsize=12)\n",
    "plt.ylabel(\"统计数\", fontsize=12)\n",
    "\n",
    "# 设置 x 轴刻度和标签\n",
    "plt.xticks(x, bin_labels, ha='center', fontsize=10, rotation=45)\n",
    "\n",
    "# 添加图例\n",
    "plt.legend(fontsize=12)\n",
    "\n",
    "# 添加网格\n",
    "plt.grid(axis='y', linestyle='--', linewidth=0.5)\n",
    "\n",
    "# 在柱状图上标注数值\n",
    "for bars in [bars1, bars2]:\n",
    "    for bar in bars:\n",
    "        height = bar.get_height()\n",
    "        plt.text(bar.get_x() + bar.get_width() / 2, height,\n",
    "                 f'{height:.0f}', ha='center', va='bottom', fontsize=10, color='black')\n",
    "\n",
    "# 显示图形\n",
    "plt.tight_layout()\n",
    " \n",
    "# save_path = title\n",
    "# if save_path:\n",
    "#     plt.savefig(save_path, dpi=300)  # 保存为高分辨率图片\n",
    "#     print(f\"Figure saved to {save_path}\")\n",
    "\n",
    "plt.show()"
   ],
   "id": "eafe5a0f4d95d120",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[29, 0, 0, 31, 376, 2976, 18306, 73469, 66172, 3639, 118, 4, 0, 0, 0]]\n",
      "[[131690, 0, 0, 0, 0, 0, 0, 0, 31152, 2237, 41, 0, 0, 0, 0]]\n",
      "[2.9000e+01 0.0000e+00 0.0000e+00 3.1000e+01 3.7600e+02 2.9760e+03\n",
      " 1.8306e+04 7.3469e+04 6.6172e+04 3.6390e+03 1.1800e+02 4.0000e+00\n",
      " 0.0000e+00 0.0000e+00 0.0000e+00]\n",
      "[1.3169e+05 0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00\n",
      " 0.0000e+00 0.0000e+00 3.1152e+04 2.2370e+03 4.1000e+01 0.0000e+00\n",
      " 0.0000e+00 0.0000e+00 0.0000e+00]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 1600x800 with 1 Axes>"
      ],
      "image/png": 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radOmkkr3BWjUqJEWLVqkkSNHSpL1c9evX6958+bpgQceuKYbhffp00f9+vXT22+/XW5/gZKSEj311FPy8fHRzp07rYHHE088odtvv13jx4/XZ599Zj3+kUce0fTp0zV58mQNHTpUDz30kM37EydOqF69enr33XdVrVo1bdq0yfrFcM+ePdW1a1d99913Nksx5eTk6Nlnn9WUKVMkSZmZmWrRooXeeecdPffccwoJCZGDg4M+/vhjOTg4qHbt2iooKFD//v2tm09L0muvvaYxY8YoOTnZug9CUVGR8vPz5eXlpWnTpmnSpEnlZixI0oULF+Tq6lqpkORic+bM0ZIlSy65ubUkjRs3Ts7OzvL19S1XN2rUKNWvX98ael2tZs2aadOmTdb3M2fOVGJiohISEqyhRkXLT/Xr10/+/v7q1q2bHBwc1Lt3b/Xv318jR46Un5+fzcydO+64Q7/++qv13BMnTpQLFwwGg7744gslJiZaA7mKfPfddxUGdV27dq3w+ObNm6tVq1YaNmyY7rrrLvXv31/u7u7KyclRQkKCmjZtav3vrcymTZu0efNmRUREqHfv3rr77rvVpk0bm2N+/vlndevWTQaDQdWrV1dYWJjNslYrVqzQsmXLNGnSJHl5eVnLR40axZJUAAAAdopQAwAAADeli9f8LwslpNLlkaTSL0vLvmRdtmyZzbnnz5/XDz/8IElKTk6+3l21io6O1qRJkyqs27Fjh5KSktSiRQt99NFHNnWurq76+eefbcruvfdeSbJupv3792XLAl28dJUknTp1ytpWRWMfPXq09d++vr7q37+/Ro8erbVr11oDkIs3bP59OHMpTk5ONl9KVxRoSFJaWppCQkLUpUsXnT17VmfOnLlkm2FhYfL09JRU+uW3JGuosXfvXs2bN08eHh6X7GNeXp4yMjI0fvx4a1nZv0tKSlRUVKTc3Fz997//lY+PT6XGWcbV1VXLly9XrVq19Oqrr2rw4MEVLj+VlJSks2fPatGiRTKbzVq0aJE6d+5sMwOha9euGjRokE6cOKFhw4YpNzdXHh4eSkxMtC6VdrHAwEDdc889FS7DViYiIkINGzYsV36pa1WzZk2tX7/eOotizpw5kqTNmzdbg5uL/zuUSmcTbd682bp3SufOnW3qZ86cqaFDh8rJyUkrV67Uk08+qbCwMA0YMMB6jNFo1LJly/Too4/abEr+7rvvEmoAAADYKUINAAAA3JSSkpIkSZ9++mmF9b/fN+Gbb77RkiVLtGHDBv32229ycCjdnq64uPia9uty7V385fnvlY1nx44d2rFjR7n634+nWrVql31fpqSkRJ9//rm+/vprbdq0SampqdYZFb/vq5+fn4KCgmzKypZySktLK9e22WyWxWKRi4uL9XpeTnFxsXJzcy/ZV6l0+ah69epJKl0SatSoUZc8dvXq1erUqZO1L5KsIcfRo0c1ceJEubm5XTbUKCgo0LvvvluurizUyM/P1/PPP3/VoYZUuqfEQw89pJUrV6pRo0bW2TcXhxqbNm0qtwzXqlWrtGrVKuv7QYMGaciQITp+/LgGDx6s5cuXq3379kpKSrIJoa7G1S4/JanCZaEOHjx4yfbS09Pl4OBQ4YbvO3fu1Isvvih3d3d99913l/zMy6nMPQcAAIAbD6EGAAAAbmpnz5612ReiIs8995ymTJmi2NhY9erVS3FxcWrXrp3NL7+vlZSUlEvWXW45oDLvvfeehg0bdk36YrFY1LNnT3377be666671K9fP7Vs2VLh4eEV9qWiQKasrKIvkD/88EONGDHikp9/qX1I8vLyKpypcfr0aZ04ccI666Tsl/hffPGFzRfjO3bs0JAhQ2zCioKCAjk5OVnLevbsaZ0ZcSkBAQEKDw9XYmLiZY+7koMHDyo9Pd1m1sSKFSvUv39/ZWdn69NPP9XDDz+s77//XpJtqPGPf/xD9913nwwGgxo0aKDOnTtbZ0EsXbpUDz30kHX5qjp16uiOO+7Q5MmTlZ6eXuEG9pV1tctPXcqPP/4oSbrtttvK1aWnpysoKMgaol2sefPmWr9+vVxdXdWkSZOr+kwAAADYN0INAAAA3JTKfs1/6NAh3XrrrdbyrKwszZ07Vw0aNFCnTp104sQJTZkyRR06dNCaNWusvzbPycm5ZNuV/QV4UVGRzXuTyaRdu3Zd7VAk2Y7n95YuXaqMjAwNGjToqtrcuHGjvv32W/Xr108ff/yxtXzfvn0VHp+VlaW0tDTrckEX96d27drlju/Tp4/at28vV1dXOTg4WK/tRx99pClTpuj7779XrVq1JJUGLEVFRcrJybnkzImVK1dKklq2bCnp/0ONsiCmTNl1L1tiSyqdqXHxBtzXU3FxsYxGoySpW7du2rdvnwYNGqR27drp1KlTGj58uObNm6e4uDgtXLjQ+retaPkpFxcXubi46MMPP9SJEyf08MMPW8c2YcIE3XLLLTbBxbBhw/TAAw9o37596tq1q8LCwv6SMVfkwoULWrJkiZo1a6aaNWuWq09PT1dISMglz4+Li7N5f/Hf80pKSkoq31EAAADcUAg1AAAAcFN66KGH9NFHH2nChAlavHixnJ2dJZWu9f/iiy/q/fffV6dOnXTq1ClJpXsClH3pnp+fX27Jn4uVfYH/+z0nioqK5ORU+n/BAwMDdeDAAZv68ePHKysr6w+Np0WLFoqMjNSiRYs0fPhw67JPqamp+uc//6n69etr8ODBV9VmRkaGJFmDBal0P5HLtfP6669r5syZkqRz584pISFBLi4u5TaBLmv34rbLlM2AiYyMtAkjrmTx4sWSZP0Sv2xj7Uu5eCaG2Wy+7Cbh10JCQoKWLFmiX3/9VefPn5eDg4MCAwM1bdo0Pfjggzp9+rQaNmyozMxMPffcc3rvvffKzSaRVGH4YjQaVb9+fXXu3FnBwcGKiorSli1bNH/+fJvjevbsqdDQUKWlpemZZ575w2O53IyMu+66q1JtjB49WhcuXLhkPzIyMio1O6lM2RJil1JcXKy8vDz99ttvSk9PV+PGjSvdNgAAAG4chBoAAAC4KbVp00YDBgzQ7Nmz1axZM3Xq1Enp6elatmyZmjZtqieffFKS1KhRI4WEhGjBggVycHCQp6envv76a2VnZ8tgMMhkMpVru2yZn3feeUenT5+Wp6entm7dqv79++vxxx+XJPXo0UNz5szRyJEj1aNHDy1btkyzZs1S69attWnTpqsej4ODg2bOnKmuXbuqRYsWevDBB+Xt7a0lS5aooKBA77333lW32bp1a3l5eendd9/VmTNnVFhYqK+++sq6pNTvx16tWjUtWLBASUlJio6O1nfffafU1FS99tprqlGjxlV//tXYu3evvvvuOzVv3twa6JRt+n6pZawunilz4cIF634a10t+fr7+97//qVGjRnrllVfUt2/fcjMUBgwYoAYNGthsdl2mbOPuipbeGjNmjMaMGaO0tDS9+OKLWrx4sQwGg5544gmtWLFCs2bNkpubm959913r/iajR4/WrbfeKn9//6sey4EDByrcKLyisoosXrxYkydPVrNmzSocq9FoVH5+/mVnavxeRaHGxbM3Tp48qaioKOtxsbGxlW4bAAAANw5CDQAAANy0Zs6cqWbNmmn69OmaMWOGgoOD9eyzz+q1116Tl5eXpNIvkFeuXKnhw4dr+fLlcnBwUIcOHfTmm2+qa9eu+vHHH3X+/HlVr17d2q7BYNAXX3yhF198UUuWLFFWVpYiIyNVt25d6zHvvvuuzGazpk2bpg8//FB33nmnfv31V7311lt/KNSQpPbt2+uXX37R6NGjtXz5cjk6Oio+Pl5jxoyxWWKrskJCQvS///1P//3vfzV37lx5eHioW7dueuWVV9S8eXN9++23evvtt62zXPz8/DRv3jwNGTJEs2fPVlhYmD788EM999xzf2g8lWWxWPTCCy/IYrFoyJAh1vLnn39ejz766CXPMxgM+vnnn3X27Fn9/PPPV/0ld0FBwRVng1ysd+/eCg8PV5cuXS55zDvvvGPzPjc3V46Ojrpw4YKWLVsmSZfcdPzMmTOaMGGClixZoqeeekrjxo3T/PnztW/fPplMJj366KNaunSpBg8erJYtW6pfv35q27atvvzyS0VGRpZrryz0KQuzUlNTtXnzZknSokWLym0KL0mZmZk6dOiQpk6dqvz8/HL7u1gsFk2ePFnDhw+Xn5+fli5dap29dLEFCxZI0lXN1ImJiSkXqpQFcMXFxQoLC1Pv3r119uxZde7cWQMHDqx02wAAALhxGCxXs/AoAAAAAFSgbdu2OnbsmI4dO3bFY8+ePauEhAS5urpW+IX2t99+q//9738aO3asdRaBxWJRYWGhzGazjEajevXqZd07Y9WqVerWrZvq1Kmjffv2WffSuJJjx46pbt261l/zjx07Vq+88kolR1waeEVERJRbRuxaKtukvkxQUJBSUlKsy1JlZWXpf//7n7766it9+eWXqlmzpiZOnKgHHnhAUulsmunTp+utt96S2WzWhAkTrCHTxIkTNXz4cHl4eGj8+PHlwqdTp04pODhYL774olJTU/Xdd9/Jycmpwo27L2axWFRcXKyCggLl5uZay1etWqUxY8Zo48aNCg0N1apVq9SoUSNrfWFhoVq3bq0jR47owoULcnFx0c6dOxUdHf2Hr9+rr76q119/XampqTZ7vQAAAMB+MVMDAAAAwF/KaDRq5MiRcnd3l5OTU4UbqwcFBenDDz+0vrdYLDKbzcrPz5fZbFaLFi2soUbnzp21adMmmUymSgcaUuksgGbNmqmgoEDdu3fXiy++eFXjKCwstNmX43q4++67NX/+fNWpU0etWrXSkCFDbPbZcHJy0uTJk1VcXKxp06bpscces86ckUpnWyxfvlxt2rTRxIkTbWYLDRs2TE2bNtV//vMf3X///eU+u2xs+fn5WrRo0Z8ah8Vi0bp167Rx40bdd999mjVrlnXvlDLOzs7q1auXpk6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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "execution_count": 3
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python [conda env:llama_env] *",
   "language": "python",
   "name": "conda-env-llama_env-py"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
